We propose a human pose estimation framework that solves the task in the regression-based fashion. Unlike previous regression-based methods, which often fall behind those state-of-the-art methods, we formulate the pose estimation task into a sequence prediction problem that can effectively be solved by transformers. Our framework is simple and direct, bypassing the drawbacks of the heatmap-based pose estimation. Moreover, with the attention mechanism in transformers, our proposed framework is able to adaptively attend to the features most relevant to the target keypoints, which largely overcomes the feature misalignment issue of previous regression-based methods and considerably improves the performance. Importantly, our framework can inherently take advantages of the structured relationship between keypoints. Experiments on the MS-COCO and MPII datasets demonstrate that our method can significantly improve the state-of-the-art of regression-based pose estimation and perform comparably with the best heatmap-based pose estimation methods.

我们提出了一种人体姿态估计框架，该框架以回归为基础，采用转换器有效地解决了姿态估计任务，使用序列预测问题的方法可以避免热图姿态估计带来的缺陷，并且通过关注机制可以适应地关注与目标关键点最相关的特征，从而大大克服了以前回归方法中存在的特征不对齐问题，而且我们的框架能够固有地利用关键点之间的结构关系，在MS-COCO和MPII数据集上的实验证明了我们的方法可以显著提高基于回归的姿态估计的最新技术水平，并且表现与最好的基于热图的姿态估计方法相当。

基于Transformer的人体姿势直接估计（TFPose）